A learning scheme for low-speed precision tracking control of hybrid stepping motors

Servo control of the hybrid stepping motor is complicated due to its highly nonlinear torque-current-position characteristics, especially under low operating speeds. This paper presents a simple and efficient control algorithm for the high-precision tracking control of hybrid stepping motors. The principles of learning control have been exploited to minimize the motor's torque ripple, which is periodic and nonlinear in the system states, with specific emphasis on low-speed situations. The proposed algorithm utilizes a fixed proportional-derivative (PD) feedback controller to stabilize the transient dynamics of the servomotor and the feedforward learning controller to compensate for the effect of the torque ripple and other disturbances for improved tracking accuracy. The stability and convergence performance of the learning control scheme is presented. It has been found that all error signals in the learning control system are bounded and the motion trajectory converges to the desired value asymptotically. The experimental results demonstrated the effectiveness and performance of the proposed algorithm.